SAMHD1 as the Potential Link Between SARS-CoV-2 Infection and Neurological Complications
Bibliographic record
Abstract
The recent pandemic of coronavirus infectious illness 2019 (COVID19) triggered by SARS-CoV-2 has rapidly spread around the globe, generating in severe events an acute, highly lethal pneumonia and death. In the past two hitherto similar CoVs, the severe acute respiratory syndrome CoV (SARS-CoV-1) and Middle East respiratory syndrome CoV (MERS-CoV) also gained universal attention as they produced clinical symptoms similar to those of SARS-CoV-2 utilizing angiotensin-converting enzyme 2 (ACE2) receptor and dipeptidyl peptidase 4 (DPP4) to go into the cells. COVID-19 may also present with overtly neurological symptoms. The proper understanding of the expression and dissemination of ACE2 in central and peripheral nerve systems is crucial to understand better the neurological morbidity caused by COVID-19. Using the STRING bioinformatic tool and references through text mining tools associated to Coronaviruses, we identified SAMHD1 as the probable link to neurological symptoms. Paralleled to the response to influenza A virus and, specifically, respiratory syncytial virus, SARS-CoV-2 evokes a response that needs robust induction of a subclass of cytokines, including the Type I and, obviously, Type III interferons as well as a few chemokines. We correlate ACE2 to the pathogenesis and neurologic complications of COVID-19 and found that SAMHD1 links to NF-κB pathway. No correlation was found with other molecules associated with Coronavirus infection, including ADAR, BST2, IRF3, IFITM3, ISG15, MX1, MX2, RNASEL, RSAD2, and VPRBP. We suggest that SAMHD1 is the molecule that may be behind the mechanisms of the neurological complications associated with COVID-19.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".